RTX 3060 12GB 12GB

// weights AWQ 4-bit for StarCoder2 15B (15B params)
weights = params × bits ÷ 8
        = 15 × 4.25 ÷ 8
        = 7.97 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.97 ≤ 8.85  → FITS
headroom  = 0.88 GB of weights budget left

// weights Q4_K_M for Phi-4 (14.7B params)
weights = params × bits ÷ 8
        = 14.7 × 4.5 ÷ 8
        = 8.27 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
8.27 ≤ 8.85  → FITS
headroom  = 0.58 GB of weights budget left

// weights Q6_K for Qwen 3.5 9B (9B params)
weights = params × bits ÷ 8
        = 9 × 6.56 ÷ 8
        = 7.38 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.38 ≤ 8.85  → FITS
headroom  = 1.47 GB of weights budget left

// weights Q6_K for Gemma 2 9B (9B params)
weights = params × bits ÷ 8
        = 9 × 6.56 ÷ 8
        = 7.38 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.38 ≤ 8.85  → FITS
headroom  = 1.47 GB of weights budget left

// weights FP8/INT8 for Llama 3.1 8B (8B params)
weights = params × bits ÷ 8
        = 8 × 8 ÷ 8
        = 8.00 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
8.00 ≤ 8.85  → FITS
headroom  = 0.85 GB of weights budget left

// weights FP8/INT8 for Granite 8B Code (8B params)
weights = params × bits ÷ 8
        = 8 × 8 ÷ 8
        = 8.00 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
8.00 ≤ 8.85  → FITS
headroom  = 0.85 GB of weights budget left

// weights Q8_0 for Mistral 7B v0.3 (7.2B params)
weights = params × bits ÷ 8
        = 7.2 × 8.5 ÷ 8
        = 7.65 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.65 ≤ 8.85  → FITS
headroom  = 1.20 GB of weights budget left

// weights Q8_0 for Qwen 2.5 7B (7B params)
weights = params × bits ÷ 8
        = 7 × 8.5 ÷ 8
        = 7.44 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.44 ≤ 8.85  → FITS
headroom  = 1.41 GB of weights budget left

// weights FP16/BF16 for Gemma 4 E4B (4B params)
weights = params × bits ÷ 8
        = 4 × 16 ÷ 8
        = 8.00 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
8.00 ≤ 8.85  → FITS
headroom  = 0.85 GB of weights budget left

// weights FP16/BF16 for Phi-4 Mini (3.8B params)
weights = params × bits ÷ 8
        = 3.8 × 16 ÷ 8
        = 7.60 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
7.60 ≤ 8.85  → FITS
headroom  = 1.25 GB of weights budget left

// weights FP16/BF16 for Llama 3.2 3B (3.21B params)
weights = params × bits ÷ 8
        = 3.21 × 16 ÷ 8
        = 6.42 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
6.42 ≤ 8.85  → FITS
headroom  = 2.43 GB of weights budget left

// weights FP16/BF16 for Llama 3.2 1B (1.23B params)
weights = params × bits ÷ 8
        = 1.23 × 16 ÷ 8
        = 2.46 GB

// budget on RTX 3060 12GB (12GB) at ctx 8K, conc 1, 15% safety
kv_cache  = 0.05 GB    (1× at ctx 8K)
overhead  = 1.30 GB    (runtime, cuda, allocator)
safety    = 1.80 GB    (15% of 12GB)
budget    = vram − safety − kv − overhead
          = 12 − 1.80 − 0.05 − 1.30
          = 8.85 GB

// fit decision
2.46 ≤ 8.85  → FITS
headroom  = 6.39 GB of weights budget left